TY  - BOOK
AU  - Lin,Zhouchen
AU  - Li,Huan
AU  - Fang,Cong
TI  - Alternating Direction Method of Multipliers for Machine Learning
SN  - 9789811698408
U1  - 006.31 23
PY  - 2022///
CY  - Singapore
PB  - Springer Nature Singapore, Imprint: Springer
KW  - Machine learning
KW  - Mathematical optimization
KW  - Computer science-Mathematics
KW  - Mathematics-Data processing
KW  - Machine Learning
KW  - Optimization
KW  - Mathematical Applications in Computer Science
KW  - Computational Mathematics and Numerical Analysis
N1  - Chapter 1. Introduction -- Chapter 2. Derivations of ADMM -- Chapter 3. ADMM for Deterministic and Convex Optimization -- Chapter 4. ADMM for Nonconvex Optimization -- Chapter 5. ADMM for Stochastic Optimization -- Chapter 6. ADMM for Distributed Optimization -- Chapter 7. Practical Issues and Conclusions
N2  - Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time
UR  - https://doi.org/10.1007/978-981-16-9840-8
ER  -